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Cerebro vs Kibana: What are the differences?
Introduction:
Cerebro and Kibana are both powerful tools used for data exploration and visualization in the field of data analytics. While they serve similar purposes, there are key differences between the two that set them apart from each other. The following paragraphs will outline these key differences.
- User Interface and Experience: Cerebro offers a more visually appealing and customizable user interface, allowing users to design dashboards and visualizations according to their specific needs. On the other hand, Kibana provides a simpler and more intuitive user interface that focuses on ease of use and quick access to essential features.
- Data Sources and Integrations: Kibana is specifically built for use with Elasticsearch, making it a powerful choice for organizations already utilizing the Elasticsearch stack. Cerebro, on the other hand, supports multiple data sources, including Elasticsearch, but also other popular databases such as SQL and NoSQL, making it a more versatile tool for data analysis across different platforms and systems.
- Alerting and Monitoring: Cerebro offers advanced alerting and monitoring capabilities, allowing users to set up custom alerts based on pre-defined conditions and receive notifications when specific events occur. Kibana, on the other hand, provides limited alerting features and focuses more on real-time monitoring and analysis of data.
- Machine Learning Integration: Cerebro provides seamless integration with machine learning frameworks, enabling users to leverage advanced analytics techniques such as anomaly detection, clustering, and predictive modeling. In contrast, Kibana does not offer native machine learning integration and relies on third-party plugins or custom implementations for incorporating machine learning capabilities.
- Data Transformation and Enrichment: Cerebro offers built-in data transformation and enrichment capabilities, allowing users to manipulate and enhance data during the analysis process. This includes features such as data cleansing, aggregation, and data augmentation. Kibana, while providing basic data manipulation features, does not have the same level of built-in data transformation capabilities as Cerebro.
- Scalability and Performance: Kibana, being developed by Elastic, the same company behind Elasticsearch, is optimized for scalability and performance in large-scale data environments. It is capable of handling high data volumes with ease. While Cerebro can also handle large datasets, its performance may not be as efficient as Kibana when dealing with extremely high data volumes.
In Summary, Cerebro provides a visually appealing user interface with support for multiple data sources, advanced alerting and monitoring capabilities, seamless machine learning integration, built-in data transformation features, and reasonable scalability. Kibana, on the other hand, focuses on simplicity, specifically designed for Elasticsearch users, provides basic alerting and monitoring features, limited native machine learning integration, basic data manipulation capabilities, and optimized scalability and performance in large-scale data environments.
From a StackShare Community member: “We need better analytics & insights into our Elasticsearch cluster. Grafana, which ships with advanced support for Elasticsearch, looks great but isn’t officially supported/endorsed by Elastic. Kibana, on the other hand, is made and supported by Elastic. I’m wondering what people suggest in this situation."
For our Predictive Analytics platform, we have used both Grafana and Kibana
- Grafana based demo video: https://www.youtube.com/watch?v=tdTB2AcU4Sg
- Kibana based reporting screenshot: https://imgur.com/vuVvZKN
Kibana has predictions
and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).
For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:
- Creating and organizing visualization panels
- Templating the panels on dashboards for repetetive tasks
- Realtime monitoring, filtering of charts based on conditions and variables
- Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics
After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .
I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.
Kibana should be sufficient in this architecture for decent analytics, if stronger metrics is needed then combine with Grafana. Datadog also offers nice overview but there's no need for it in this case unless you need more monitoring and alerting (and more technicalities).
@Kibana, of course, because @Grafana looks like amateur sort of solution, crammed with query builder grouping aggregates, but in essence, as recommended by CERN - KIbana is the corporate (startup vectored) decision.
Furthermore, @Kibana comes with complexity adhering ELK stack, whereas @InfluxDB + @Grafana & co. recently have become sophisticated development conglomerate instead of advancing towards a understandable installation step by step inheritance.
Pros of Cerebro
Pros of Kibana
- Easy to setup88
- Free65
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs9
- Supports Plugins6
- Dev Tools4
- More "user-friendly"3
- Can build dashboards3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
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Cons of Cerebro
Cons of Kibana
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3